He is an IEEE Life Fellow, an IEICE Fellow, was elected to the Engineering Academy of Japan (1992), and received the 2005 Eduard Rhein Technology Award. He also spent 15 years at the IBM Research Center, Yorktown Heights, NY, and was the Founding Director of IBM Tokyo Research Laboratory. Hisashi Kobayashi is the Sherman Fairchild University Professor Emeritus at Princeton University, where he was previously Dean of the School of Engineering and Applied Science. With a solutions manual, lecture slides, supplementary materials, and MATLAB programs all available online, it is ideal for classroom teaching as well as a valuable reference for professionals. The book will be useful to students and researchers in such areas as communications, signal processing, networks, machine learning, bioinformatics, and econometrics and mathematical finance. Advanced topics include: īayesian inference and conjugate priors Chernoff bound and large deviation approximation Principal component analysis and singular value decomposition Autoregressive moving average (ARMA) time series Maximum likelihood estimation and the Expectation-Maximization (EM) algorithm Brownian motion, geometric Brownian motion, and Ito process Black–Scholes differential equation for option pricing Hidden Markov model (HMM) and estimation algorithms Bayesian networks and sum-product algorithm Markov chain Monte Carlo methods Wiener and Kalman filters Queueing and loss networks Probability, Random Processes, and Statistical Analysis Together with the fundamentals of probability, random processes, and statistical analysis, this insightful book also presents a broad range of advanced topics and applications not covered in other textbooks.
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